Multimodal Authoring System for Artificial Companion Persona
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current authoring tools for multimodal output systems are limited, requiring entirely human-directed input, lacking support for multimodal inputs like voice, visual, and gesture files, and failing to provide adequate suggestions or corrections for maintaining the persona and constraints of artificial companions, leading to inefficiencies and potential violations of content guidelines.
Innovation Solution
A multimodal authoring system that utilizes voice recognition, visual effect, facial expression, and mobility files, along with artificial neural networks for automatic generation and testing of presentation conversation files, providing autocomplete functionality, context-aware suggestions, and automatic correction to ensure compliance with persona and content guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional authoring tools are used with human-directed input only, then authoring control is maintained, but authoring efficiency is low and time-consuming
Solution Approach 1:
The system enables self-service authoring by allowing the artificial companion to automatically generate its own presentation conversation files using neural network processing. The companion can autonomously create content based on its learned characteristics without requiring manual authoring for every interaction, significantly improving productivity while maintaining quality through automated self-generation.
2Adaptability or versatility
If speech recognition is implemented, then voice input is enabled, but system complexity increases
Solution Approach 1:
The authoring system is designed with multi-functionality to handle diverse input modalities including speech, text, and other forms of input through a unified architecture. The renderer module can process various file types (voice files, visual effect files, facial expression files, gesture files, mobility files) through common processing pipelines, enabling versatile input support without proportionally increasing system complexity.
3Productivity
If multimodal files are processed automatically, then content creation efficiency is improved, but accuracy of persona maintenance may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the neural network continuously learns from generated content and adjusts its processing to better maintain persona characteristics. The automatic testing system validates generated presentation conversation files against defined constraints and provides feedback for refinement, ensuring that efficiency gains do not compromise persona accuracy and constraint adherence.
4Loss of time
If manual authoring is used, then content accuracy is maintained, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-defining persona characteristics, constraints, and content guidelines that are stored and reused for generating presentation conversation files. This preliminary setup enables rapid automatic generation of consistent, high-quality content that adheres to defined standards, reducing both time consumption and maintaining reliability through reusable templates and predefined quality criteria.
Data Source
AI summary
Systems and methods for authoring and modifying presentation conversation files are disclosed. Exemplary implementations may: receive, at a renderer module, voice files, visual effect files, facial expression files, and/or mobility files; analyze, by the language processor module, the voice files, the visual effect files, the facial expression files, and/or mobility files follow guidelines of a multimodal authoring system; generate, by the renderer module, one or more presentation conversation files based at least in part on the received voice files, visual effect files, facial expression files, and/or mobility files; test, at an automatic testing system, the one or more presentation conversation files to verify correct operation of a computing device that receives the one or more presentation conversation files as an input; and identify, by a multimodal review module, changes to be made to the voice input files, the visual effect files, the facial expression files, and/or the mobility files.